Grasp Planning to Maximize Task Coverage
Document Type
Article
Publication Date
7-2015
Keywords
task-oriented grasping, task modeling, task disturbance distribution, grasp quality measure
Digital Object Identifier (DOI)
https://doi.org/10.1177/0278364915583880
Abstract
This paper proposes a task-oriented grasp quality metric based on distribution of task disturbance, which could be used to search for a grasp that covers the most significant part of the disturbance distribution. Rather than using a uniformly distributed task wrench space, this paper models a manipulation task with a non-parametric statistical distribution model built from the disturbance data captured during the task demonstrations. The grasp resulting from maximizing the proposed grasp quality criterion is prone to increasing the coverage of most frequent disturbances. To reduce the computational complexity of the search in a high-dimensional robotic hand configuration space, as well as to avoid the correspondence problem, the candidate grasps are computed from a reduced configuration space that is confined by a set of given thumb placements and thumb directions. The proposed approach has been tested both in simulation and on a real robotic system. In simulation, the approach was validated with a Barrett hand and a Shadow hand in several manipulation tasks. Experiments on a physical robotic platform verified the consistency between the proposed grasp metric and the success rate.
Rights Information
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
The International Journal of Robotics Research, v. 34, issue 9, p. 1195-1210.
Scholar Commons Citation
Lin, Yun and Sun, Yu, "Grasp Planning to Maximize Task Coverage" (2015). Computer Science and Engineering Faculty Publications. 56.
https://digitalcommons.usf.edu/esb_facpub/56
